In the widely used methodology of functional magnetic resonance imaging (fMRI), the formal control of power (i.e., 1-false-negative rate) has received less attention than the control of false-positive rate. The control of power is necessary for quantifying confidence in negative fMRI results in both basic scientific and clinical settings. A striking example of the clinical utility of being able to inform fMRI experimental design by a priori power calculations is in pre-operative fMRI testing of neurological surgery candidates for localization of function. Clearly in such circumstances a false negative could have undesirable consequences for the patient. In basic science, a priori power calculations for fMRI experiments would afford the ability to refute brain-cognition models based on negative results with a desired confidence. Specific Aim 1 is to formulate an algorithm for determining voxel-wise power of parametric (i.e., t and F) tests on fMRI data. In contrast to previous methods, this algorithm will account for the temporal autocorrelation structure of the noise, the complete experimental design, the hypothesis of interest, assumed signal:noise ratios, and the filtering properties of the neural activity-to-fMRI transform. Specific Aim 2 is to investigate saturation characteristics of the transformation of neural activity change to fMRI signal change. Better characterization of saturation will improve predictions of fMRI effect magnitude and hence improve the accuracy of power calculations. Saturation over meaningful physiological ranges will be examined by testing for deviations from superposition of fMRI responses (in primary sensori-motor and early visual regions) elicited by sets of one, two, or three closely spaced (1 sec visual stimuli. Sepcific Aim 3 is to investigate the dependence of the power of event-related fMRI designs on their temporal structure. The ability to improve power of experimental designs by manipulation of their temporal structure (without changing the cost) will make high power fMRI experiments more feasible.